A mobile services recommendation system fuses implicit and explicit user trust relationships

doi:10.3233/AIS-200585

	A mobile services recommendation system fuses implicit and explicit user trust relationships
	Luo, Pengcheng 1,2; Zhang, Jilin 1,2,4; Wan, Jian 1,2,3; Zhao, Nailiang 1,2; Ren, Zujie 5; Zhou, Li 1,2; Shen, Jing 1,2,4
	2021
发表期刊	JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS
ISSN	1876-1364
卷号	13 期号:1 页码:21-35
摘要	In recent years, with the development of advanced mobile applications, people's various daily behavior data, such as geographic location, social information, hobbies, are more easily collected. To process these data, data cross-boundary fusion has become a key technology, and there are some challenges, such as solving the problems of the cross-boundary business integrity, cross-boundary value complementarity and so on. Mobile Services Recommendation requires improved recommendation accuracy. User trust is an effective measure of information similarity between users. Using trust can effectively improve the accuracy of recommendations. The existing methods have low utilization of general trust data, sparseness of trust data, and lack of user trust characteristics. Therefore, a method needs to be proposed to make up for the shortcomings of explicit trust relationships and improve the accuracy of user interest feature completion. In this paper, a recommendation model is proposed to mine the implicit trust relationships from user data and integrate the explicit social information of users. First, the rating prediction model was improved using the traditional Singular Value Decomposition (SVD) model, and the implicit trust relationships were mined from the user's historical data. Then, they were fused with the explicit social trust relationships to obtain a crossover data fusion model. We tested the model using three different orders of magnitude. We compared the user preference prediction accuracies of two models: one that does not integrate social information and one that integrates social information. The results show that our model improves the user preference prediction accuracy and has higher accuracy for cold start users. On the three data sets, the average error is reduced by 2.29%, 5.44% and 4.42%, suggesting that it is an effective data crossover fusion technology.
关键词	Collaborative filtering data management data mining matrix factorization recommendation system social network
DOI	10.3233/AIS-200585
收录类别	SCI
语种	英语
资助项目	National Key Technology Research and Development Program[2019YFB2102100] ; National Natural Science Foundation of China[61672200] ; National Natural Science Foundation of China[61972358] ; Key Technology Research and Development Program of the Zhejiang Province[2019C03134] ; Key Technology Research and Development Program of the Zhejiang Province[2019C03135] ; Zhejiang Natural Science Funds[LY18F020014] ; Zhejiang Natural Science Funds[LY17F020029] ; Zhejiang Natural Science Funds[LY16F020018] ; State Key Laboratory of Computer Architecture Project[CARCH201712] ; Hangzhou Dianzi University Postgraduate Research Innovation Fund Program[CXJJ2018052]
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Telecommunications
WOS记录号	WOS:000613204400004
出版者	IOS PRESS
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/16194
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhao, Nailiang
作者单位	1.Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou 310018, Peoples R China 2.Minist Educ, Key Lab Complex Syst Modeling & Simulat, Hangzhou 310018, Peoples R China 3.Zhejiang Univ Sci & Technol, Hangzhou 310023, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China 5.Zhejiang Lab, Hangzhou 310000, Peoples R China
推荐引用方式 GB/T 7714	Luo, Pengcheng,Zhang, Jilin,Wan, Jian,et al. A mobile services recommendation system fuses implicit and explicit user trust relationships[J]. JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS,2021,13(1):21-35.
APA	Luo, Pengcheng.,Zhang, Jilin.,Wan, Jian.,Zhao, Nailiang.,Ren, Zujie.,...&Shen, Jing.(2021).A mobile services recommendation system fuses implicit and explicit user trust relationships.JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS,13(1),21-35.
MLA	Luo, Pengcheng,et al."A mobile services recommendation system fuses implicit and explicit user trust relationships".JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS 13.1(2021):21-35.